{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('happiness_train_complete.csv', encoding='gbk')#读取训练数据\n",
    "df_test = pd.read_csv('happiness_test_complete.csv', encoding='gbk')#读取测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>happiness</th>\n",
       "      <th>survey_type</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>survey_time</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth</th>\n",
       "      <th>nationality</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbor_familiarity</th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>59</td>\n",
       "      <td>2015/8/4 14:18</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>52</td>\n",
       "      <td>85</td>\n",
       "      <td>2015/7/21 15:04</td>\n",
       "      <td>1</td>\n",
       "      <td>1992</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>85.0</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>83</td>\n",
       "      <td>126</td>\n",
       "      <td>2015/7/21 13:24</td>\n",
       "      <td>2</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>79</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>51</td>\n",
       "      <td>2015/7/25 17:33</td>\n",
       "      <td>2</td>\n",
       "      <td>1943</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>2015/8/10 9:50</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  happiness  survey_type  province  city  county      survey_time  \\\n",
       "0   1          4            1        12    32      59   2015/8/4 14:18   \n",
       "1   2          4            2        18    52      85  2015/7/21 15:04   \n",
       "2   3          4            2        29    83     126  2015/7/21 13:24   \n",
       "3   4          5            2        10    28      51  2015/7/25 17:33   \n",
       "4   5          4            1         7    18      36   2015/8/10 9:50   \n",
       "\n",
       "   gender  birth  nationality        ...         neighbor_familiarity  \\\n",
       "0       1   1959            1        ...                            4   \n",
       "1       1   1992            1        ...                            3   \n",
       "2       2   1967            1        ...                            4   \n",
       "3       2   1943            1        ...                            3   \n",
       "4       2   1994            1        ...                            2   \n",
       "\n",
       "   public_service_1  public_service_2 public_service_3  public_service_4  \\\n",
       "0                50                60               50                50   \n",
       "1                90                70               70                80   \n",
       "2                90                80               75                79   \n",
       "3               100                90               70                80   \n",
       "4                50                50               50                50   \n",
       "\n",
       "   public_service_5  public_service_6  public_service_7  public_service_8  \\\n",
       "0              30.0                30                50                50   \n",
       "1              85.0                70                90                60   \n",
       "2              80.0                90                90                90   \n",
       "3              80.0                90                90                80   \n",
       "4              50.0                50                50                50   \n",
       "\n",
       "   public_service_9  \n",
       "0                50  \n",
       "1                60  \n",
       "2                75  \n",
       "3                80  \n",
       "4                50  \n",
       "\n",
       "[5 rows x 140 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查\n",
    "df_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "happiness                  0\n",
       "survey_type                0\n",
       "province                   0\n",
       "city                       0\n",
       "county                     0\n",
       "survey_time                0\n",
       "gender                     0\n",
       "birth                      0\n",
       "nationality                0\n",
       "religion                   0\n",
       "religion_freq              0\n",
       "edu                        0\n",
       "edu_other               7997\n",
       "edu_status              1120\n",
       "edu_yr                  1972\n",
       "income                     0\n",
       "political                  0\n",
       "join_party              7176\n",
       "floor_area                 0\n",
       "property_0                 0\n",
       "property_1                 0\n",
       "property_2                 0\n",
       "property_3                 0\n",
       "property_4                 0\n",
       "property_5                 0\n",
       "property_6                 0\n",
       "property_7                 0\n",
       "property_8                 0\n",
       "property_other          7934\n",
       "                        ... \n",
       "m_political                0\n",
       "m_work_14                  0\n",
       "status_peer                0\n",
       "status_3_before            0\n",
       "view                       0\n",
       "inc_ability                0\n",
       "inc_exp                    0\n",
       "trust_1                    0\n",
       "trust_2                    0\n",
       "trust_3                    0\n",
       "trust_4                    0\n",
       "trust_5                    0\n",
       "trust_6                    0\n",
       "trust_7                    0\n",
       "trust_8                    0\n",
       "trust_9                    0\n",
       "trust_10                   0\n",
       "trust_11                   0\n",
       "trust_12                   0\n",
       "trust_13                   0\n",
       "neighbor_familiarity       0\n",
       "public_service_1           0\n",
       "public_service_2           0\n",
       "public_service_3           0\n",
       "public_service_4           0\n",
       "public_service_5           0\n",
       "public_service_6           0\n",
       "public_service_7           0\n",
       "public_service_8           0\n",
       "public_service_9           0\n",
       "Length: 140, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()#统计缺失参数的数目\n",
    "# df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 4    4818\n",
       " 5    1410\n",
       " 3    1159\n",
       " 2     497\n",
       " 1     104\n",
       "-8      12\n",
       "Name: happiness, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看lable的分布\n",
    "y_train=df_train[\"happiness\"]#将df_train中happiness这一栏的数据赋给y_train\n",
    "y_train.value_counts()#统计y_train中各个数值的数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#处理异常值\n",
    "df_train = df_train.loc[df_train['happiness'] != -8]#将happiness=-8的值剔除\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>happiness</th>\n",
       "      <th>survey_type</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth</th>\n",
       "      <th>nationality</th>\n",
       "      <th>religion</th>\n",
       "      <th>...</th>\n",
       "      <th>neighbor_familiarity</th>\n",
       "      <th>public_service_1</th>\n",
       "      <th>public_service_2</th>\n",
       "      <th>public_service_3</th>\n",
       "      <th>public_service_4</th>\n",
       "      <th>public_service_5</th>\n",
       "      <th>public_service_6</th>\n",
       "      <th>public_service_7</th>\n",
       "      <th>public_service_8</th>\n",
       "      <th>public_service_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.00000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "      <td>7988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4001.603906</td>\n",
       "      <td>3.867927</td>\n",
       "      <td>1.405984</td>\n",
       "      <td>15.161617</td>\n",
       "      <td>42.583250</td>\n",
       "      <td>70.64647</td>\n",
       "      <td>1.530295</td>\n",
       "      <td>1964.702303</td>\n",
       "      <td>1.374061</td>\n",
       "      <td>0.776915</td>\n",
       "      <td>...</td>\n",
       "      <td>3.723335</td>\n",
       "      <td>70.840260</td>\n",
       "      <td>68.214572</td>\n",
       "      <td>62.787932</td>\n",
       "      <td>66.346895</td>\n",
       "      <td>62.821670</td>\n",
       "      <td>67.090761</td>\n",
       "      <td>66.120931</td>\n",
       "      <td>65.651352</td>\n",
       "      <td>67.188408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2309.864374</td>\n",
       "      <td>0.818717</td>\n",
       "      <td>0.491112</td>\n",
       "      <td>8.915476</td>\n",
       "      <td>27.184344</td>\n",
       "      <td>38.73718</td>\n",
       "      <td>0.499113</td>\n",
       "      <td>16.848414</td>\n",
       "      <td>1.529899</td>\n",
       "      <td>1.053975</td>\n",
       "      <td>...</td>\n",
       "      <td>1.142888</td>\n",
       "      <td>21.156018</td>\n",
       "      <td>20.500041</td>\n",
       "      <td>24.723590</td>\n",
       "      <td>22.028845</td>\n",
       "      <td>23.440032</td>\n",
       "      <td>21.566658</td>\n",
       "      <td>23.065318</td>\n",
       "      <td>23.803476</td>\n",
       "      <td>22.472591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1921.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2001.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>55.750000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4003.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>73.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6002.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>104.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>134.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id    happiness  survey_type     province         city  \\\n",
       "count  7988.000000  7988.000000  7988.000000  7988.000000  7988.000000   \n",
       "mean   4001.603906     3.867927     1.405984    15.161617    42.583250   \n",
       "std    2309.864374     0.818717     0.491112     8.915476    27.184344   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%    2001.750000     4.000000     1.000000     7.000000    18.000000   \n",
       "50%    4003.500000     4.000000     1.000000    15.000000    42.000000   \n",
       "75%    6002.250000     4.000000     2.000000    22.000000    65.000000   \n",
       "max    8000.000000     5.000000     2.000000    31.000000    89.000000   \n",
       "\n",
       "           county       gender        birth  nationality     religion  \\\n",
       "count  7988.00000  7988.000000  7988.000000  7988.000000  7988.000000   \n",
       "mean     70.64647     1.530295  1964.702303     1.374061     0.776915   \n",
       "std      38.73718     0.499113    16.848414     1.529899     1.053975   \n",
       "min       1.00000     1.000000  1921.000000    -8.000000    -8.000000   \n",
       "25%      37.00000     1.000000  1952.000000     1.000000     1.000000   \n",
       "50%      73.00000     2.000000  1965.000000     1.000000     1.000000   \n",
       "75%     104.00000     2.000000  1977.000000     1.000000     1.000000   \n",
       "max     134.00000     2.000000  1997.000000     8.000000     1.000000   \n",
       "\n",
       "             ...         neighbor_familiarity  public_service_1  \\\n",
       "count        ...                  7988.000000       7988.000000   \n",
       "mean         ...                     3.723335         70.840260   \n",
       "std          ...                     1.142888         21.156018   \n",
       "min          ...                    -8.000000         -3.000000   \n",
       "25%          ...                     3.000000         60.000000   \n",
       "50%          ...                     4.000000         79.500000   \n",
       "75%          ...                     5.000000         80.000000   \n",
       "max          ...                     5.000000        100.000000   \n",
       "\n",
       "       public_service_2  public_service_3  public_service_4  public_service_5  \\\n",
       "count       7988.000000       7988.000000       7988.000000       7988.000000   \n",
       "mean          68.214572         62.787932         66.346895         62.821670   \n",
       "std           20.500041         24.723590         22.028845         23.440032   \n",
       "min           -3.000000         -3.000000         -3.000000         -3.000000   \n",
       "25%           60.000000         50.000000         60.000000         55.750000   \n",
       "50%           70.000000         70.000000         70.000000         70.000000   \n",
       "75%           80.000000         80.000000         80.000000         80.000000   \n",
       "max          100.000000        100.000000        100.000000        100.000000   \n",
       "\n",
       "       public_service_6  public_service_7  public_service_8  public_service_9  \n",
       "count       7988.000000       7988.000000       7988.000000       7988.000000  \n",
       "mean          67.090761         66.120931         65.651352         67.188408  \n",
       "std           21.566658         23.065318         23.803476         22.472591  \n",
       "min           -3.000000         -3.000000         -3.000000         -3.000000  \n",
       "25%           60.000000         60.000000         60.000000         60.000000  \n",
       "50%           70.000000         70.000000         70.000000         70.000000  \n",
       "75%           80.000000         80.000000         80.000000         80.000000  \n",
       "max          100.000000        100.000000        100.000000        100.000000  \n",
       "\n",
       "[8 rows x 136 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()#显示数据集的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4    4818\n",
      "5    1410\n",
      "3    1159\n",
      "2     497\n",
      "1     104\n",
      "Name: happiness, dtype: int64\n"
     ]
    },
    {
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PMwIAoHxQGPTsKN8BAKDM8Tpc3FokHe2c21/SAZJONLPJkm6S9G3n3HhJqySd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\nkNKdV0f+97mbIpfuVmPpAT4yoHxtm+osyZUSUHJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8eb31ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看分布情况\n",
    "f,ax=plt.subplots(1,2,figsize=(18,8))#创建1*2个子图  figsize是定义图像大小  ax就是图像的总称，ax[0]左,ax[1]右\n",
    "print(df_train['happiness'].value_counts())\n",
    "#df_tarin['happiness'].value_counts()统计happiness栏目中各个值出现的次数\n",
    "df_train['happiness'].value_counts().plot.pie(autopct='%1.1f%%',ax=ax[0])\n",
    "#plot.pie()绘制饼图autopct 控制饼图内百分比设置,可以使用format字符串或者format function'%1.1f'指小数点前后位数(没有用空格补齐)\n",
    "ax[0].set_title('happiness')\n",
    "ax[0].set_ylabel('test')#设置竖直方向的标题\n",
    "sns.countplot('happiness',data=df_train,ax=ax[1])#画条形图\n",
    "ax[1].set_title('happiness')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将训练数据和测试数据连在一起\n",
    "total = pd.concat((df_train,df_test)).reset_index(drop=True)\n",
    "#concat（）表的拼接 axis参数为0是相同字段的表首尾相接  axis=1是横向表拼接\n",
    "#concat（）容易出现逻辑错误，通常在后面用reset_index做处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#数据进行处理\n",
    "total=total[total[\"happiness\"]!=-8]\n",
    "i=1\n",
    "total[\"public_service\"]=0#新增一列栏目为total[\"public_service\"],并将初始值设置为0，用以存储对所有public_service1-9系列的值\n",
    "while i<10:                                                                                                                                                                                                                                                                                                                                                                                                                                                \n",
    "    total[\"public_service\"]=total[\"public_service\"]+total[\"public_service_\"+str(i)]#将所有的值public_service1-9系列的值加起来\n",
    "    total=total.drop([\"public_service_\"+str(i)],axis=1)#drop删除行或者列 axis=1时删除列，=0时删除行 加完值后便将public_service1-9列删除\n",
    "    i=i+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#接下来的同上都是将所有的系列值加和\n",
    "i=1\n",
    "total[\"trust\"]=0\n",
    "while i<14:\n",
    "    total[\"trust\"]=total[\"trust\"]+total[\"trust_\"+str(i)]\n",
    "    total=total.drop([\"trust_\"+str(i)],axis=1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "total[\"invest\"]=0\n",
    "while i<9:\n",
    "    total[\"invest\"]=total[\"invest\"]+total[\"invest_\"+str(i)]\n",
    "    total=total.drop([\"invest_\"+str(i)],axis=1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=1\n",
    "total[\"insur\"]=0\n",
    "while i<5:\n",
    "    total[\"insur\"]=total[\"insur\"]+total[\"insur_\"+str(i)]\n",
    "    total=total.drop([\"insur_\"+str(i)],axis=1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=1\n",
    "total[\"leisure\"]=0\n",
    "while i<13:\n",
    "    total[\"leisure\"]=total[\"leisure\"]+total[\"leisure_\"+str(i)]\n",
    "    total=total.drop([\"leisure_\"+str(i)],axis=1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=1\n",
    "total[\"media\"]=0\n",
    "while i<7:\n",
    "    total[\"media\"]=total[\"media\"]+total[\"media_\"+str(i)]\n",
    "    total=total.drop([\"media_\"+str(i)],axis=1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "total[\"property\"]=0\n",
    "while i<9:\n",
    "    total[\"property\"]=total[\"property\"]+total[\"property_\"+str(i)]\n",
    "    total=total.drop([\"property_\"+str(i)],axis=1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total.to_csv('total.csv', index=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train=total[total[\"id\"]<8001]#将数据集分成训练集和测试集两部分\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_test=total[total[\"id\"]>8000]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birth                 0\n",
       "car                   0\n",
       "city                  0\n",
       "class                 0\n",
       "class_10_after        0\n",
       "class_10_before       0\n",
       "class_14              0\n",
       "county                0\n",
       "daughter              0\n",
       "depression            0\n",
       "edu                   0\n",
       "edu_other          2965\n",
       "edu_status          449\n",
       "edu_yr              784\n",
       "equity                0\n",
       "f_birth               0\n",
       "f_edu                 0\n",
       "f_political           0\n",
       "f_work_14             0\n",
       "family_income         0\n",
       "family_m              0\n",
       "family_status         0\n",
       "floor_area            0\n",
       "gender                0\n",
       "happiness          2968\n",
       "health                0\n",
       "health_problem        0\n",
       "height_cm             0\n",
       "house                 0\n",
       "hukou                 0\n",
       "                   ... \n",
       "s_edu               649\n",
       "s_hukou             649\n",
       "s_income            649\n",
       "s_political         649\n",
       "s_work_exper        649\n",
       "s_work_status      2009\n",
       "s_work_type        2009\n",
       "socia_outing          0\n",
       "social_friend       301\n",
       "social_neighbor     301\n",
       "socialize             0\n",
       "son                   0\n",
       "status_3_before       0\n",
       "status_peer           0\n",
       "survey_time           0\n",
       "survey_type           0\n",
       "view                  0\n",
       "weight_jin            0\n",
       "work_exper            0\n",
       "work_manage        1889\n",
       "work_status        1890\n",
       "work_type          1889\n",
       "work_yr            1890\n",
       "public_service        0\n",
       "trust                 0\n",
       "invest                0\n",
       "insur                 0\n",
       "leisure               0\n",
       "media                 0\n",
       "property              0\n",
       "Length: 85, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()#找出空值较多的列\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 total Percent  Percent_tmp\n",
      "edu_other         7988  99.96%     0.999624\n",
      "invest_other      7988  99.64%     0.996370\n",
      "property_other    7988  99.17%     0.991738\n",
      "join_party        7988  89.70%     0.896970\n",
      "s_work_status     7988  67.95%     0.679519\n",
      "s_work_type       7988  67.95%     0.679519\n",
      "work_yr           7988  63.12%     0.631197\n",
      "work_type         7988  63.12%     0.631197\n",
      "work_status       7988  63.12%     0.631197\n",
      "work_manage       7988  63.12%     0.631197\n",
      "edu_yr            7988  24.66%     0.246620\n",
      "marital_now       7988  22.13%     0.221332\n",
      "s_work_exper      7988  21.48%     0.214822\n",
      "s_birth           7988  21.48%     0.214822\n",
      "s_edu             7988  21.48%     0.214822\n",
      "s_hukou           7988  21.48%     0.214822\n",
      "s_political       7988  21.48%     0.214822\n",
      "s_income          7988  21.48%     0.214822\n",
      "edu_status        7988  14.02%     0.140210\n",
      "minor_child       7988  13.33%     0.133325\n",
      "marital_1st       7988  10.35%     0.103530\n",
      "social_neighbor   7988   9.95%     0.099524\n",
      "social_friend     7988   9.95%     0.099524\n",
      "hukou_loc         7988   0.05%     0.000501\n",
      "family_income     7988   0.01%     0.000125\n"
     ]
    }
   ],
   "source": [
    "#缺失值填充\n",
    "miss_data = data_train.isnull().sum().sort_values(ascending=False)  # 缺失值数量\n",
    "total_miss = data_train.isnull().count()  # 总数量\n",
    "miss_data_tmp = (miss_data / total_miss).sort_values(ascending=False)  # 缺失值占比\n",
    "\n",
    "# 添加百分号\n",
    "def precent(X):\n",
    "    X = '%.2f%%' % (X * 100)\n",
    "    return X\n",
    "miss_precent = miss_data_tmp.map(precent)\n",
    "# 根据缺失值占比倒序排序\n",
    "miss_data_precent = pd.concat([total_miss, miss_precent, miss_data_tmp], axis=1, keys=[\n",
    "                              'total', 'Percent', 'Percent_tmp']).sort_values(by='Percent_tmp', ascending=False)\n",
    "# 有缺失值的变量打印出来\n",
    "print(miss_data_precent[miss_data_precent['Percent'] != '0.00%'])\n",
    "#* 将缺失值比例大于15%的数据全部删除，剩余数值型变量用众数填充、类别型变量用None填充。*\n",
    "\n",
    "drop_columns = miss_data_precent[miss_data_precent['Percent_tmp'] > 0.15].index\n",
    "data_train = data_train.drop(drop_columns, axis=1)\n",
    "data_test = data_test.drop(drop_columns, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_test=data_test.drop([\"happiness\"],axis=1)#将测试集的happine栏目删除\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu_status              449\n",
       "minor_child             382\n",
       "marital_1st             301\n",
       "social_neighbor         301\n",
       "social_friend           301\n",
       "house                     0\n",
       "height_cm                 0\n",
       "health_problem            0\n",
       "hukou                     0\n",
       "f_work_14                 0\n",
       "health                    0\n",
       "gender                    0\n",
       "hukou_loc                 0\n",
       "floor_area                0\n",
       "family_status             0\n",
       "family_m                  0\n",
       "id                        0\n",
       "family_income             0\n",
       "property                  0\n",
       "f_political               0\n",
       "inc_exp                   0\n",
       "f_edu                     0\n",
       "f_birth                   0\n",
       "equity                    0\n",
       "edu                       0\n",
       "depression                0\n",
       "daughter                  0\n",
       "county                    0\n",
       "class_14                  0\n",
       "class_10_before           0\n",
       "                       ... \n",
       "income                    0\n",
       "media                     0\n",
       "leisure                   0\n",
       "insur                     0\n",
       "invest                    0\n",
       "trust                     0\n",
       "public_service            0\n",
       "work_exper                0\n",
       "weight_jin                0\n",
       "view                      0\n",
       "survey_type               0\n",
       "survey_time               0\n",
       "status_peer               0\n",
       "status_3_before           0\n",
       "son                       0\n",
       "socialize                 0\n",
       "socia_outing              0\n",
       "religion_freq             0\n",
       "religion                  0\n",
       "relax                     0\n",
       "province                  0\n",
       "political                 0\n",
       "neighbor_familiarity      0\n",
       "nationality               0\n",
       "marital                   0\n",
       "m_work_14                 0\n",
       "m_political               0\n",
       "m_edu                     0\n",
       "m_birth                   0\n",
       "birth                     0\n",
       "Length: 66, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum().sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别型变量:['survey_time'] 数值型变量:['birth', 'car', 'city', 'class', 'class_10_after', 'class_10_before', 'class_14', 'county', 'daughter', 'depression', 'edu', 'edu_status', 'equity', 'f_birth', 'f_edu', 'f_political', 'f_work_14', 'family_income', 'family_m', 'family_status', 'floor_area', 'gender', 'health', 'health_problem', 'height_cm', 'house', 'hukou', 'hukou_loc', 'id', 'inc_ability', 'inc_exp', 'income', 'learn', 'm_birth', 'm_edu', 'm_political', 'm_work_14', 'marital', 'marital_1st', 'minor_child', 'nationality', 'neighbor_familiarity', 'political', 'province', 'relax', 'religion', 'religion_freq', 'socia_outing', 'social_friend', 'social_neighbor', 'socialize', 'son', 'status_3_before', 'status_peer', 'survey_type', 'view', 'weight_jin', 'work_exper', 'public_service', 'trust', 'invest', 'insur', 'leisure', 'media', 'property']\n"
     ]
    }
   ],
   "source": [
    "# 类别型变量\n",
    "#dtypes函数判断变量类型\n",
    "class_variable = [\n",
    "    col for col in data_test.columns if data_train[col].dtypes == 'O']\n",
    "# 数值型变量\n",
    "numerical_variable = [\n",
    "    col for col in data_test.columns if data_train[col].dtypes != 'O']  # 大写o\n",
    "print('类别型变量:%s' % class_variable, '数值型变量:%s' % numerical_variable)\n",
    "\n",
    "# 数值型变量用中位数填充，test集中最后一列为预测价格，所以不可以填充\n",
    "from sklearn.preprocessing import Imputer\n",
    "#Imputer填充模块\n",
    "padding = Imputer(strategy='most_frequent')#用众数进行填充，strategy=‘most——frequent’众数，mean是均值，medain中位数\n",
    "data_train[numerical_variable] = padding.fit_transform(data_train[numerical_variable])\n",
    "data_test[numerical_variable] = padding.fit_transform(data_test[numerical_variable])\n",
    "# 类别变量用None填充\n",
    "data_train[class_variable] = data_train[class_variable].fillna('None')\n",
    "data_test[class_variable] = data_test[class_variable].fillna('None')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train['survey_time'] = pd.to_datetime(data_train['survey_time'])#将调查时间数据转化为标准格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "data_train['survey_time'] = data_train['survey_time'].dt.year#提取调查年份\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#添加年龄特征\n",
    "data_train['age']=data_train['survey_time']-data_train['birth']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>car</th>\n",
       "      <th>city</th>\n",
       "      <th>class</th>\n",
       "      <th>class_10_after</th>\n",
       "      <th>class_10_before</th>\n",
       "      <th>class_14</th>\n",
       "      <th>county</th>\n",
       "      <th>daughter</th>\n",
       "      <th>depression</th>\n",
       "      <th>...</th>\n",
       "      <th>weight_jin</th>\n",
       "      <th>work_exper</th>\n",
       "      <th>public_service</th>\n",
       "      <th>trust</th>\n",
       "      <th>invest</th>\n",
       "      <th>insur</th>\n",
       "      <th>leisure</th>\n",
       "      <th>media</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1967.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    birth  car  city  class  class_10_after  class_10_before  class_14  \\\n",
       "0  1959.0  2.0  32.0    3.0             3.0              3.0       1.0   \n",
       "1  1992.0  2.0  52.0    6.0             8.0              4.0       5.0   \n",
       "2  1967.0  2.0  83.0    5.0             6.0              4.0       3.0   \n",
       "3  1943.0  1.0  28.0    5.0             7.0              5.0       2.0   \n",
       "4  1994.0  1.0  18.0    1.0             1.0              1.0       4.0   \n",
       "\n",
       "   county  daughter  depression  ...   weight_jin  work_exper  public_service  \\\n",
       "0    59.0       0.0         5.0  ...        155.0         1.0           420.0   \n",
       "1    85.0       0.0         3.0  ...        110.0         1.0           675.0   \n",
       "2   126.0       2.0         5.0  ...        122.0         2.0           749.0   \n",
       "3    51.0       4.0         4.0  ...        170.0         4.0           760.0   \n",
       "4    36.0       0.0         3.0  ...        110.0         6.0           450.0   \n",
       "\n",
       "   trust  invest  insur  leisure  media  property   age  \n",
       "0    7.0     1.0    5.0     33.0   23.0       1.0  56.0  \n",
       "1   43.0     1.0    4.0     39.0   14.0       1.0  23.0  \n",
       "2   27.0     1.0    6.0     45.0   15.0       2.0  48.0  \n",
       "3   44.0     1.0    8.0     43.0   11.0       1.0  72.0  \n",
       "4   44.0     1.0    7.0     40.0   20.0       1.0  21.0  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据\n",
    "data_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将测试集重复上述操作\n",
    "data_test['survey_time'] = pd.to_datetime(data_test['survey_time'])\n",
    "data_test['survey_time'] = data_test['survey_time'].dt.year\n",
    "data_test['age']=data_test['survey_time']-data_test['birth']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total=[data_train,data_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#把年龄分为多个阶段\n",
    "for data in total:\n",
    "    data.loc[data['age']<=20,'age']=0\n",
    "    data.loc[(data['age'] > 20) & (data['age'] <= 40), 'age'] = 1#如果a列的值等于m，修改b列的值为n\n",
    "    data.loc[(data['age'] > 40) & (data['age'] <= 60), 'age'] = 2\n",
    "    data.loc[(data['age'] > 60) & (data['age'] <= 80), 'age'] = 3\n",
    "    data.loc[ data['age'] > 80, 'age'] = 4\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a8ebe772e8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RvT179lBeXk6PHj1YsGDBfvsKsRw7OHlYAWXzi5jpL5nZkej222+nf//+vP/++wfsS1+O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\na04eZmaWNScPMzPLmpOHmZll7f8D853oEWLCE9gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8ebe6a518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('age',hue='happiness',data=data_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_train=data_train[data_train['happiness']>0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mr8ARwHxJe1F62uVzW7LQrX6rjSCfARqGOwkzMzMzMzPbtkVEO/BJ4FrgYUpP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cHPWoDmWmuzEflJIPSrrMmofXjAOus/IQkirSwsy76XhceShK3sGKyPhROB+buepQnJ9q39CO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\nMzMzs8r9F4fQO2Txgn4SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8ed6e3e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#相关性矩阵\n",
    "corrmat = data_train.corr()#corr()显示任意两个变量的相关系数\n",
    "f, ax = plt.subplots(figsize=(20, 15))\n",
    "sns.heatmap(corrmat, square=True,cmap='YlGnBu')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nKFnV96Ff71E7dfoS5cs+TX4fD0wmIw3qVGL/oQiC/win2YvWKx2a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PYO9rgWNJfjL/O/p3SlshQtW5wCHq6cOnCSKUFfsHXVBp5vWku3fMmJKRSwyZdVzwD+L1XFvaBt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\noHzFUrYyHGgrw+W3ve+9dD/03ffDsYF4sNzrmQOewNPAM8AU7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nH51LWcOX4OJxcn3vmw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\nbD3OrOPn6VejvEN55QIGeu84QY9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Feg5rxw/fbKdEmcJY0lVOHjmHOTWdo3ut+7rD+614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mg0sWb117Twv5WP147m3akvc/Lkb7pkK5K0JRWj0cSytW8QGvYYi993bOvd3xsYFbrCoa0B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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8ee71ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看与happiness较相关的20个特征\n",
    "data_train=data_train[data_train[\"happiness\"]!=-8]\n",
    "k = 20#number of variables for heatmap\n",
    "corrmat = data_train.corr()\n",
    "cols = corrmat.nlargest(k, \"happiness\")[\"happiness\"].index\n",
    "cm = np.corrcoef(data_train[cols].values.T)\n",
    "f, ax = plt.subplots(figsize=(20, 15))\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10\n",
    "                                                                              }, yticklabels=cols.values, xticklabels=cols.values,cmap='YlGnBu')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y=data_train.happiness\n",
    "ind =['depression','class','health','equity','family_status','health_problem','edu','house','weight_jin','age',\"class_10_after\",\"public_service\",\"media\",\"class_10_before\",\"relax\",\"learn\",\"class_14\",\"socialize\",\"political\",\"view\"]\n",
    "x=data_train[ind]\n",
    "x_test_data=data_test[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5202420456977728\n",
      "0.5029113064560344\n",
      "0.4879655142861643\n",
      "0.4756270131832589\n",
      "0.46035925550243273\n",
      "0.4275487715693297\n",
      "0.4640949925811085\n",
      "0.5269590707384426\n",
      "0.39641195210451263\n",
      "0.4397393577127548\n",
      "0.5588224122972885\n",
      "0.49412979920954037\n",
      "0.461442630814231\n",
      "0.4757544787453474\n",
      "0.4533084009066584\n",
      "catboost 0.4763544667869919 [0.5202420456977728, 0.5029113064560344, 0.4879655142861643, 0.4756270131832589, 0.46035925550243273, 0.4275487715693297, 0.4640949925811085, 0.5269590707384426, 0.39641195210451263, 0.4397393577127548, 0.5588224122972885, 0.49412979920954037, 0.461442630814231, 0.4757544787453474, 0.4533084009066584]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from catboost import Pool, CatBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.externals import joblib\n",
    "kfold = KFold(n_splits=15, shuffle = True, random_state= 12)\n",
    "model = CatBoostRegressor(colsample_bylevel=0.1,thread_count=6,silent=True,iterations=800, \n",
    "                          depth=5, \n",
    "                          learning_rate=0.051, \n",
    "                          loss_function='RMSE',\n",
    "                          l2_leaf_reg = 3)\n",
    "mse = []\n",
    "i=0\n",
    "for train, test in kfold.split(x):\n",
    "    X_train = x.iloc[train]\n",
    "    y_train = y.iloc[train]\n",
    "    X_test = x.iloc[test]\n",
    "    y_test = y.iloc[test]\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    err = mean_squared_error(y_true=y_test,y_pred=y_pred)\n",
    "    mse.append(err)\n",
    "    print(err)\n",
    "    joblib.dump(filename=\"cat\"+str(i),value=model)\n",
    "    i+=1\n",
    "print(\"catboost\",np.mean(mse),mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgboost 0.5525525276970447\n",
      "xgboost 0.4827762342841232\n",
      "xgboost 0.4904637673884577\n",
      "xgboost 0.4967549758695447\n",
      "xgboost 0.4274864407768519\n",
      "xgboost 0.5078926606283863\n",
      "xgboost 0.4595418705876522\n",
      "xgboost 0.5058318137070302\n",
      "xgboost 0.4624982182955757\n",
      "xgboost 0.5156472391783032\n",
      "xgboost 0.4375789239514562\n",
      "xgboost 0.4854299682045809\n",
      "xgboost 0.41298930997229455\n",
      "xgboost 0.48062263270614913\n",
      "xgboost 0.48876715934394027\n",
      "xgboost 0.480455582839426 [0.5525525276970447, 0.4827762342841232, 0.4904637673884577, 0.4967549758695447, 0.4274864407768519, 0.5078926606283863, 0.4595418705876522, 0.5058318137070302, 0.4624982182955757, 0.5156472391783032, 0.4375789239514562, 0.4854299682045809, 0.41298930997229455, 0.48062263270614913, 0.48876715934394027]\n"
     ]
    }
   ],
   "source": [
    "#xgboost\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.model_selection import KFold\n",
    "kfold = KFold(n_splits=15, shuffle = True, random_state= 11)\n",
    "model = XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=0.1,\n",
    "       colsample_bytree=0.971, gamma=0.11, learning_rate=0.069, max_delta_step=0,\n",
    "       max_depth=3, min_child_weight=1, missing=None, n_estimators=499,\n",
    "       n_jobs=-1, nthread=50, objective='reg:linear', random_state=0,\n",
    "       reg_alpha=0.1, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
    "       silent=True, subsample=1.0)\n",
    "mse = []\n",
    "i = 0\n",
    "for train, test in kfold.split(x):\n",
    "    X_train = x.iloc[train]\n",
    "    y_train = y.iloc[train]\n",
    "    X_test = x.iloc[test]\n",
    "    y_test = y.iloc[test]\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    xg_mse = mean_squared_error(y_true=y_test,y_pred=y_pred)\n",
    "    mse.append(xg_mse)\n",
    "    print(\"xgboost\",xg_mse)\n",
    "    joblib.dump(filename=\"xg\"+str(i),value=model)\n",
    "    i+=1\n",
    "print(\"xgboost\",np.mean(mse),mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbdt 0.5388472500297493\n",
      "gbdt 0.5273023757495239\n",
      "gbdt 0.5055512670471131\n",
      "gbdt 0.4890400011117602\n",
      "gbdt 0.47074012318299724\n",
      "gbdt 0.4602681620785064\n",
      "gbdt 0.4672603677460071\n",
      "gbdt 0.539235848496186\n",
      "gbdt 0.3997828083248622\n",
      "gbdt 0.45042478604792346\n",
      "gbdt 0.5669076977379999\n",
      "gbdt 0.5093762901545391\n",
      "gbdt 0.46934858182085293\n",
      "gbdt 0.49061418039519106\n",
      "gbdt 0.48108034010077594\n",
      "gbdt 0.49105200533493254 [0.5388472500297493, 0.5273023757495239, 0.5055512670471131, 0.4890400011117602, 0.47074012318299724, 0.4602681620785064, 0.4672603677460071, 0.539235848496186, 0.3997828083248622, 0.45042478604792346, 0.5669076977379999, 0.5093762901545391, 0.46934858182085293, 0.49061418039519106, 0.48108034010077594]\n"
     ]
    }
   ],
   "source": [
    "#gbdt\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.model_selection import KFold\n",
    "kfold = KFold(n_splits=15, shuffle = True, random_state= 12)\n",
    "model = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
    "             learning_rate=0.051, loss='ls', max_depth=4, max_features=10,\n",
    "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "             min_impurity_split=None, min_samples_leaf=1,\n",
    "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "             n_estimators=600, presort='auto', random_state=3,\n",
    "             subsample=0.98, verbose=0, warm_start=False)\n",
    "\n",
    "mse = []\n",
    "i = 0\n",
    "for train, test in kfold.split(x):\n",
    "    X_train = x.iloc[train]\n",
    "    y_train = y.iloc[train]\n",
    "    X_test = x.iloc[test]\n",
    "    y_test = y.iloc[test]\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    gbdt_mse = mean_squared_error(y_true=y_test,y_pred=y_pred)\n",
    "    mse.append(gbdt_mse)\n",
    "    print(\"gbdt\",gbdt_mse)\n",
    "    joblib.dump(filename=\"gbdt\"+str(i),value=model)\n",
    "    i+=1\n",
    "print(\"gbdt\",np.mean(mse),mse)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cat mse: 0.4242171490513304\n",
      "xg mse: 0.4220463405669043\n",
      "gbdt mse: 0.3253183136585947\n",
      "lr mse: 0.3756816994621879\n",
      "[15:37:33] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.4241793037478784\n",
      "xg mse: 0.41986810970628413\n",
      "gbdt mse: 0.32064665688087246\n",
      "lr mse: 0.37309375711341725\n",
      "[15:37:37] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.39146389972937695\n",
      "xg mse: 0.3867970730569908\n",
      "gbdt mse: 0.2918573908283256\n",
      "lr mse: 0.3429351283450138\n",
      "[15:37:40] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.4510361827647191\n",
      "xg mse: 0.4480502913484316\n",
      "gbdt mse: 0.3417351475878543\n",
      "lr mse: 0.39882421709444005\n",
      "[15:37:44] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.390465752398689\n",
      "xg mse: 0.39034222162443677\n",
      "gbdt mse: 0.3049007974956397\n",
      "lr mse: 0.3506083575855483\n",
      "[15:37:47] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.4289266054251832\n",
      "xg mse: 0.4268961014258336\n",
      "gbdt mse: 0.3199811240719974\n",
      "lr mse: 0.3743413118349149\n",
      "[15:37:51] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.3972182802129997\n",
      "xg mse: 0.39298319616407423\n",
      "gbdt mse: 0.30612672831483484\n",
      "lr mse: 0.3531865565253252\n",
      "[15:37:54] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.44382669636602823\n",
      "xg mse: 0.4430098677867381\n",
      "gbdt mse: 0.3463640387685832\n",
      "lr mse: 0.3985456475537321\n",
      "[15:37:58] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.47301461365450886\n",
      "xg mse: 0.4675193031383062\n",
      "gbdt mse: 0.35420116646065414\n",
      "lr mse: 0.4159885774186868\n",
      "[15:38:01] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "\n",
      "cat mse: 0.48737902442520076\n",
      "xg mse: 0.4841653026606186\n",
      "gbdt mse: 0.3772834310764876\n",
      "lr mse: 0.435138565901116\n",
      "\n",
      "\n",
      "catmse: 0.4311727507775915\n",
      "xgmse: 0.4281677807478618\n",
      "gbdtmse: 0.32884147951438436\n",
      "lrmse: 0.38183438188343816\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.051, loss='ls', max_depth=4, max_features=10,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=600, presort='auto', random_state=3,\n",
       "             subsample=0.98, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#带权平均融合CatBoostRegressor + xgboost + gbdt现有模型\n",
    "from catboost import Pool, CatBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.externals import joblib\n",
    "kfold = KFold(n_splits=10, shuffle = True, random_state= 110)\n",
    "catmse = []\n",
    "xgmse = []\n",
    "gbdtmse = []\n",
    "lrmse = []\n",
    "i = 0\n",
    "for train, test in kfold.split(x):\n",
    "    X_train = x.iloc[train]\n",
    "    y_train = y.iloc[train]\n",
    "    X_test = x.iloc[test]\n",
    "    y_test = y.iloc[test]\n",
    "    model.fit(X_train,y_train)\n",
    "    \n",
    "    cat = joblib.load(filename=\"cat\"+str(i))\n",
    "#     light = joblib.load(filename=\"light\"+str(i))\n",
    "    xg = joblib.load(filename=\"xg\"+str(i))\n",
    "    gbdt = joblib.load(filename=\"gbdt\"+str(i))\n",
    "    \n",
    "    catX = cat.predict(X_test)\n",
    "    cat_mse = mean_squared_error(y_true=y_test,y_pred=catX)\n",
    "    print(\"\\ncat mse:\",cat_mse)\n",
    "    catmse.append(cat_mse)\n",
    "    \n",
    "    \n",
    "    xgX = xg.predict(X_test)\n",
    "    xg_mse = mean_squared_error(y_true=y_test,y_pred=xgX)\n",
    "    print(\"xg mse:\",xg_mse)\n",
    "    xgmse.append(xg_mse)\n",
    "    \n",
    "    X_test2 = X_test.fillna(-8)\n",
    "    gbdtX = gbdt.predict(X_test2)\n",
    "    gbdt_mse = mean_squared_error(y_true=y_test,y_pred=gbdtX)\n",
    "    print(\"gbdt mse:\",gbdt_mse)\n",
    "    gbdtmse.append(gbdt_mse)\n",
    "    \n",
    "    res = np.c_[catX,xgX,gbdtX]\n",
    "    e = np.array([1/cat_mse,1/xg_mse,1/gbdt_mse])\n",
    "    y_pred = np.sum(res*e,axis=1)/sum(e)\n",
    "    lr_mse = mean_squared_error(y_true=y_test,y_pred=y_pred)\n",
    "    print(\"lr mse:\",lr_mse)\n",
    "    lrmse.append(lr_mse)\n",
    "    \n",
    "    i+=1\n",
    "    \n",
    "print(\"\\n\\ncatmse:\",np.mean(catmse))\n",
    "# print(\"lightmse:\",np.mean(lightmse))\n",
    "print(\"xgmse:\",np.mean(xgmse))\n",
    "print(\"gbdtmse:\",np.mean(gbdtmse))\n",
    "print(\"lrmse:\",np.mean(lrmse))\n",
    "\n",
    "cat = CatBoostRegressor(colsample_bylevel=0.1,thread_count=6,silent=True,iterations=800, \n",
    "                          depth=5, \n",
    "                          learning_rate=0.051, \n",
    "                          loss_function='RMSE',\n",
    "                          l2_leaf_reg = 3)\n",
    "xg = XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=0.1,\n",
    "       colsample_bytree=0.971, gamma=0.11, learning_rate=0.069, max_delta_step=0,\n",
    "       max_depth=3, min_child_weight=1, missing=None, n_estimators=499,\n",
    "       n_jobs=-1, nthread=50, objective='reg:linear', random_state=0,\n",
    "       reg_alpha=0.1, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
    "       silent=True, subsample=1.0)\n",
    "gbdt = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
    "             learning_rate=0.051, loss='ls', max_depth=4, max_features=10,\n",
    "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "             min_impurity_split=None, min_samples_leaf=1,\n",
    "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "             n_estimators=600, presort='auto', random_state=3,\n",
    "             subsample=0.98, verbose=0, warm_start=False)\n",
    "cat.fit(x,y)\n",
    "xg.fit(x,y)\n",
    "gbdt.fit(x.fillna(-8),y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cat mse: 0.46545584325880235\n",
      "xg mse: 0.4614689187922862\n",
      "gbdt mse: 0.3405495085019012\n",
      "[0.19122313 0.20526537 0.6044472 ]\n",
      "lr mse: 0.38009631262732324\n",
      "\n",
      "cat mse: 0.45844846697441194\n",
      "xg mse: 0.45796402280479065\n",
      "gbdt mse: 0.35023175528783573\n",
      "[0.21017172 0.21183831 0.57758013]\n",
      "lr mse: 0.3892365042913872\n",
      "\n",
      "cat mse: 0.44567011627933484\n",
      "xg mse: 0.4440256167604769\n",
      "gbdt mse: 0.3548639357029625\n",
      "[0.22766086 0.23379257 0.54002504]\n",
      "lr mse: 0.3903003760317102\n",
      "\n",
      "cat mse: 0.4326096889598423\n",
      "xg mse: 0.4260942148392587\n",
      "gbdt mse: 0.3223571391984186\n",
      "[0.20537851 0.22829947 0.57554259]\n",
      "lr mse: 0.3588380756751484\n",
      "\n",
      "cat mse: 0.40844304583070024\n",
      "xg mse: 0.4031821630267847\n",
      "gbdt mse: 0.3078348972293168\n",
      "[0.21640476 0.23486257 0.55892316]\n",
      "lr mse: 0.34128809181034325\n",
      "\n",
      "cat mse: 0.38819980593377945\n",
      "xg mse: 0.37899763408813403\n",
      "gbdt mse: 0.29502155850715034\n",
      "[0.21507904 0.24276323 0.53387784]\n",
      "lr mse: 0.33000928295169235\n",
      "\n",
      "cat mse: 0.4196595829928172\n",
      "xg mse: 0.41830827682019733\n",
      "gbdt mse: 0.30937665593179137\n",
      "[0.20799378 0.21016894 0.57880177]\n",
      "lr mse: 0.3480975385592756\n",
      "\n",
      "cat mse: 0.48558373912439834\n",
      "xg mse: 0.47821395197836064\n",
      "gbdt mse: 0.3740721029081992\n",
      "[0.19781896 0.22022694 0.57019704]\n",
      "lr mse: 0.4116752340152546\n",
      "\n",
      "cat mse: 0.3542880842276856\n",
      "xg mse: 0.3532498287665204\n",
      "gbdt mse: 0.26734690780271714\n",
      "[0.23165175 0.2330178  0.53129666]\n",
      "lr mse: 0.3020978807014866\n",
      "\n",
      "cat mse: 0.39870898988932385\n",
      "xg mse: 0.3914045596164385\n",
      "gbdt mse: 0.29500324476872436\n",
      "[0.20956118 0.2325444  0.55995384]\n",
      "lr mse: 0.33151178374428125\n",
      "\n",
      "cat mse: 0.49470940766569454\n",
      "xg mse: 0.49102880112511244\n",
      "gbdt mse: 0.35678645967725264\n",
      "[0.17847103 0.18764392 0.62287573]\n",
      "lr mse: 0.3964446289356253\n",
      "\n",
      "cat mse: 0.4416376808359904\n",
      "xg mse: 0.4301229157019657\n",
      "gbdt mse: 0.3268286498678773\n",
      "[0.19379912 0.22510237 0.5764114 ]\n",
      "lr mse: 0.36487666699672067\n",
      "\n",
      "cat mse: 0.4219487221363311\n",
      "xg mse: 0.4184423144536123\n",
      "gbdt mse: 0.3186851271383289\n",
      "[0.20971195 0.22455342 0.56316474]\n",
      "lr mse: 0.35694047041680893\n",
      "\n",
      "cat mse: 0.43822977846228695\n",
      "xg mse: 0.4330809196592237\n",
      "gbdt mse: 0.33692253615710777\n",
      "[0.21399497 0.23158796 0.55844927]\n",
      "lr mse: 0.37322423211512196\n",
      "\n",
      "cat mse: 0.4133142450226781\n",
      "xg mse: 0.4101841943474152\n",
      "gbdt mse: 0.31598711096441723\n",
      "[0.21983511 0.23259092 0.5521649 ]\n",
      "lr mse: 0.3513487382415974\n",
      "\n",
      "catmse: 0.4311271465062718\n",
      "xgmse: 0.42638455551870513\n",
      "gbdtmse: 0.32479117264293333\n",
      "lrmse: 0.36173238780758515\n",
      "[array([3.78050713, 3.2177545 , 3.23518447, ..., 4.1199629 , 3.92914184,\n",
      "       5.10231019])]\n",
      "[3.78050713 3.2177545  3.23518447 ... 4.1199629  3.92914184 5.10231019]\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "#LR 融合CatBoostRegressor + LightGBM + xgboost + gbdt现有模型\n",
    "\n",
    "from catboost import Pool, CatBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "kfold = KFold(n_splits=15, shuffle = True, random_state= 12)\n",
    "catmse = []\n",
    "xgmse = []\n",
    "gbdtmse = []\n",
    "lrmse = []\n",
    "i = 0\n",
    "for train, test in kfold.split(x):\n",
    "    X_train = x.iloc[train]\n",
    "    y_train = y.iloc[train]\n",
    "    X_test = x.iloc[test]\n",
    "    y_test = y.iloc[test]\n",
    "#     model.fit(X_train,y_train)\n",
    "    \n",
    "    catX = cat.predict(X_test)\n",
    "    cat_mse = mean_squared_error(y_true=y_test,y_pred=catX)\n",
    "    print(\"\\ncat mse:\",cat_mse)\n",
    "    catmse.append(cat_mse)\n",
    "    \n",
    "    xgX = xg.predict(X_test)\n",
    "    xg_mse = mean_squared_error(y_true=y_test,y_pred=xgX)\n",
    "    print(\"xg mse:\",xg_mse)\n",
    "    xgmse.append(xg_mse)\n",
    "    \n",
    "    gbdtX = gbdt.predict(X_test.fillna(-8))\n",
    "    gbdt_mse = mean_squared_error(y_true=y_test,y_pred=gbdtX)\n",
    "    print(\"gbdt mse:\",gbdt_mse)\n",
    "    gbdtmse.append(gbdt_mse)\n",
    "    \n",
    "    res = np.c_[catX,xgX,gbdtX]\n",
    "    lr = Ridge(fit_intercept=False, alpha=75)\n",
    "    lr.fit(res,y_test)\n",
    "    print(lr.coef_)\n",
    "\n",
    "    y_pred = lr.predict(res)\n",
    "    lr_mse = mean_squared_error(y_true=y_test,y_pred=y_pred)\n",
    "    print(\"lr mse:\",lr_mse)\n",
    "    lrmse.append(lr_mse)\n",
    "    joblib.dump(filename=\"lr\"+str(i),value=lr)\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\ncatmse:\",np.mean(catmse))\n",
    "print(\"xgmse:\",np.mean(xgmse))\n",
    "print(\"gbdtmse:\",np.mean(gbdtmse))\n",
    "print(\"lrmse:\",np.mean(lrmse))\n",
    "\n",
    "\n",
    "    \n",
    "prediction = []\n",
    "#for i in range(15):\n",
    "  \n",
    "catX = cat.predict(x_test_data)\n",
    "xgX = xg.predict(x_test_data)\n",
    "gbdtX = gbdt.predict(x_test_data.fillna(-8))\n",
    "res = np.c_[catX,xgX,gbdtX]\n",
    "prediction.append(lr.predict(res))\n",
    "\n",
    "print(prediction)\n",
    "\n",
    "def cut(arr):\n",
    "    arr2 = []\n",
    "    for x in arr:\n",
    "        if x<1:\n",
    "            arr2.append(1)\n",
    "        elif x>5:\n",
    "            arr2.append(5)\n",
    "        else :\n",
    "            arr2.append(x)\n",
    "    return arr2\n",
    "print(prediction[0])\n",
    "df = pd.DataFrame({'id':df_test.id, 'happniess': prediction[0]})\n",
    "df.to_csv('happiness_submit.csv', index=None)\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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